Overview

Dataset statistics

Number of variables11
Number of observations408663
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.3 MiB
Average record size in memory88.0 B

Variable types

Categorical1
DateTime1
Numeric9

Warnings

id_estacion has a high cardinality: 207 distinct values High cardinality
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
longitud is highly correlated with latitudHigh correlation
latitud is highly correlated with longitudHigh correlation
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
tmin is highly correlated with tmax and 2 other fieldsHigh correlation
longitud is highly correlated with altitud and 1 other fieldsHigh correlation
tmax is highly correlated with tmin and 2 other fieldsHigh correlation
altitud is highly correlated with tmin and 3 other fieldsHigh correlation
fecha_cnt is highly correlated with tmin and 1 other fieldsHigh correlation
latitud is highly correlated with longitud and 1 other fieldsHigh correlation
nevada is highly skewed (γ1 = 227.6590307) Skewed
prof_nieve is highly skewed (γ1 = 64.20443149) Skewed
precip has 163844 (40.1%) zeros Zeros
nevada has 408645 (> 99.9%) zeros Zeros
prof_nieve has 406496 (99.5%) zeros Zeros

Reproduction

Analysis started2021-10-09 13:05:59.156280
Analysis finished2021-10-09 13:06:16.795447
Duration17.64 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id_estacion
Categorical

HIGH CARDINALITY

Distinct207
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
SP000009981
 
6040
SP000008280
 
5807
SP000003195
 
5283
SPE00120629
 
5263
SP000060010
 
5246
Other values (202)
381024 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters4495293
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP000003195
2nd rowSP000003195
3rd rowSP000003195
4th rowSP000003195
5th rowSP000003195

Common Values

ValueCountFrequency (%)
SP0000099816040
 
1.5%
SP0000082805807
 
1.4%
SP0000031955283
 
1.3%
SPE001206295263
 
1.3%
SP0000600105246
 
1.3%
SPE001552595244
 
1.3%
SP0000080274929
 
1.2%
SP0000070384740
 
1.2%
SPE001197114734
 
1.2%
SPE001204584729
 
1.2%
Other values (197)356648
87.3%

Length

2021-10-09T13:06:16.984492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp0000099816040
 
1.5%
sp0000082805807
 
1.4%
sp0000031955283
 
1.3%
spe001206295263
 
1.3%
sp0000600105246
 
1.3%
spe001552595244
 
1.3%
sp0000080274929
 
1.2%
sp0000070384740
 
1.2%
spe001197114734
 
1.2%
spe001204584729
 
1.2%
Other values (197)356648
87.3%

Most occurring characters

ValueCountFrequency (%)
01375668
30.6%
1568756
12.7%
S408663
 
9.1%
P408663
 
9.1%
2343363
 
7.6%
E328009
 
7.3%
5204220
 
4.5%
9203451
 
4.5%
6148929
 
3.3%
8143944
 
3.2%
Other values (5)361627
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3338031
74.3%
Uppercase Letter1157262
 
25.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01375668
41.2%
1568756
17.0%
2343363
 
10.3%
5204220
 
6.1%
9203451
 
6.1%
6148929
 
4.5%
8143944
 
4.3%
3135021
 
4.0%
4127379
 
3.8%
787300
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
S408663
35.3%
P408663
35.3%
E328009
28.3%
W7522
 
0.6%
M4405
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common3338031
74.3%
Latin1157262
 
25.7%

Most frequent character per script

Common
ValueCountFrequency (%)
01375668
41.2%
1568756
17.0%
2343363
 
10.3%
5204220
 
6.1%
9203451
 
6.1%
6148929
 
4.5%
8143944
 
4.3%
3135021
 
4.0%
4127379
 
3.8%
787300
 
2.6%
Latin
ValueCountFrequency (%)
S408663
35.3%
P408663
35.3%
E328009
28.3%
W7522
 
0.6%
M4405
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4495293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01375668
30.6%
1568756
12.7%
S408663
 
9.1%
P408663
 
9.1%
2343363
 
7.6%
E328009
 
7.3%
5204220
 
4.5%
9203451
 
4.5%
6148929
 
3.3%
8143944
 
3.2%
Other values (5)361627
 
8.0%

fecha
Date

Distinct6368
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Minimum1896-11-01 00:00:00
Maximum2021-08-15 00:00:00
2021-10-09T13:06:17.071495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:17.176658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fecha_cnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.6141662
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-10-09T13:06:17.284137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q340
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.05100727
Coefficient of variation (CV)0.5655261624
Kurtosis-1.196761577
Mean26.6141662
Median Absolute Deviation (MAD)13
Skewness-7.462369122 Ɨ 10-5
Sum10876225
Variance226.53282
MonotonicityNot monotonic
2021-10-09T13:06:17.385412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
227939
 
1.9%
187919
 
1.9%
147917
 
1.9%
197916
 
1.9%
97904
 
1.9%
107899
 
1.9%
237876
 
1.9%
277873
 
1.9%
307873
 
1.9%
327867
 
1.9%
Other values (43)329680
80.7%
ValueCountFrequency (%)
17867
1.9%
27779
1.9%
37792
1.9%
47793
1.9%
57857
1.9%
67708
1.9%
77623
1.9%
87632
1.9%
97904
1.9%
107899
1.9%
ValueCountFrequency (%)
531505
 
0.4%
527841
1.9%
517829
1.9%
507826
1.9%
497814
1.9%
487837
1.9%
477826
1.9%
467812
1.9%
457824
1.9%
447832
1.9%

tmax
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct523
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.4411801
Minimum-109
Maximum444
Zeros54
Zeros (%)< 0.1%
Negative1403
Negative (%)0.3%
Memory size3.1 MiB
2021-10-09T13:06:17.482673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-109
5-th percentile81
Q1147
median199
Q3256
95-th percentile322
Maximum444
Range553
Interquartile range (IQR)109

Descriptive statistics

Standard deviation74.60750118
Coefficient of variation (CV)0.3722164334
Kurtosis-0.4007092195
Mean200.4411801
Median Absolute Deviation (MAD)54
Skewness-0.04288181682
Sum81912894
Variance5566.279233
MonotonicityNot monotonic
2021-10-09T13:06:17.583419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1652186
 
0.5%
1632176
 
0.5%
1672142
 
0.5%
1592133
 
0.5%
1712128
 
0.5%
1732116
 
0.5%
1772104
 
0.5%
1572089
 
0.5%
1832087
 
0.5%
1812079
 
0.5%
Other values (513)387423
94.8%
ValueCountFrequency (%)
-1091
< 0.1%
-1041
< 0.1%
-1021
< 0.1%
-1011
< 0.1%
-992
< 0.1%
-972
< 0.1%
-951
< 0.1%
-932
< 0.1%
-921
< 0.1%
-901
< 0.1%
ValueCountFrequency (%)
4441
< 0.1%
4421
< 0.1%
4411
< 0.1%
4321
< 0.1%
4311
< 0.1%
4301
< 0.1%
4281
< 0.1%
4272
< 0.1%
4261
< 0.1%
4252
< 0.1%

tmin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct438
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.13079971
Minimum-189
Maximum275
Zeros887
Zeros (%)0.2%
Negative26537
Negative (%)6.5%
Memory size3.1 MiB
2021-10-09T13:06:17.683765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-189
5-th percentile-8
Q152
median100
Q3149
95-th percentile202
Maximum275
Range464
Interquartile range (IQR)97

Descriptive statistics

Standard deviation64.70299443
Coefficient of variation (CV)0.6527032428
Kurtosis-0.5462835166
Mean99.13079971
Median Absolute Deviation (MAD)49
Skewness-0.1461837495
Sum40511090
Variance4186.477488
MonotonicityNot monotonic
2021-10-09T13:06:17.781004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
852402
 
0.6%
1012371
 
0.6%
972358
 
0.6%
872347
 
0.6%
792346
 
0.6%
752340
 
0.6%
832337
 
0.6%
912331
 
0.6%
932328
 
0.6%
892320
 
0.6%
Other values (428)385183
94.3%
ValueCountFrequency (%)
-1891
< 0.1%
-1811
< 0.1%
-1751
< 0.1%
-1741
< 0.1%
-1721
< 0.1%
-1711
< 0.1%
-1702
< 0.1%
-1691
< 0.1%
-1681
< 0.1%
-1661
< 0.1%
ValueCountFrequency (%)
2751
 
< 0.1%
2721
 
< 0.1%
2711
 
< 0.1%
2701
 
< 0.1%
2692
 
< 0.1%
2672
 
< 0.1%
2662
 
< 0.1%
2651
 
< 0.1%
2641
 
< 0.1%
2635
< 0.1%

precip
Real number (ℝ≥0)

ZEROS

Distinct457
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.27873823
Minimum0
Maximum1077
Zeros163844
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-10-09T13:06:17.877481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q319
95-th percentile75
Maximum1077
Range1077
Interquartile range (IQR)19

Descriptive statistics

Standard deviation31.51973907
Coefficient of variation (CV)1.936251976
Kurtosis35.76920565
Mean16.27873823
Median Absolute Deviation (MAD)3
Skewness4.346156888
Sum6652518
Variance993.4939508
MonotonicityNot monotonic
2021-10-09T13:06:17.977579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0163844
40.1%
123184
 
5.7%
214227
 
3.5%
312296
 
3.0%
410169
 
2.5%
58984
 
2.2%
68152
 
2.0%
77500
 
1.8%
86457
 
1.6%
96283
 
1.5%
Other values (447)147567
36.1%
ValueCountFrequency (%)
0163844
40.1%
123184
 
5.7%
214227
 
3.5%
312296
 
3.0%
410169
 
2.5%
58984
 
2.2%
68152
 
2.0%
77500
 
1.8%
86457
 
1.6%
96283
 
1.5%
ValueCountFrequency (%)
10771
< 0.1%
8491
< 0.1%
8111
< 0.1%
8001
< 0.1%
6901
< 0.1%
6891
< 0.1%
6711
< 0.1%
6431
< 0.1%
6411
< 0.1%
6151
< 0.1%

nevada
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0002814054612
Minimum0
Maximum17
Zeros408645
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-10-09T13:06:18.177129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05076044069
Coefficient of variation (CV)180.3818606
Kurtosis59458.75037
Mean0.0002814054612
Median Absolute Deviation (MAD)0
Skewness227.6590307
Sum115
Variance0.002576622339
MonotonicityNot monotonic
2021-10-09T13:06:18.258438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0408645
> 99.9%
74
 
< 0.1%
33
 
< 0.1%
23
 
< 0.1%
43
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
101
 
< 0.1%
141
 
< 0.1%
171
 
< 0.1%
ValueCountFrequency (%)
0408645
> 99.9%
23
 
< 0.1%
33
 
< 0.1%
43
 
< 0.1%
74
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
141
 
< 0.1%
171
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
141
 
< 0.1%
111
 
< 0.1%
101
 
< 0.1%
81
 
< 0.1%
74
 
< 0.1%
43
 
< 0.1%
33
 
< 0.1%
23
 
< 0.1%
0408645
> 99.9%

prof_nieve
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct406
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4699520143
Minimum0
Maximum2385
Zeros406496
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-10-09T13:06:18.364263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2385
Range2385
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.33949568
Coefficient of variation (CV)34.76843419
Kurtosis5632.042288
Mean0.4699520143
Median Absolute Deviation (MAD)0
Skewness64.20443149
Sum192052
Variance266.9791191
MonotonicityNot monotonic
2021-10-09T13:06:18.475712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0406496
99.5%
1502
 
0.1%
3248
 
0.1%
4183
 
< 0.1%
696
 
< 0.1%
770
 
< 0.1%
942
 
< 0.1%
1034
 
< 0.1%
1323
 
< 0.1%
1622
 
< 0.1%
Other values (396)947
 
0.2%
ValueCountFrequency (%)
0406496
99.5%
1502
 
0.1%
26
 
< 0.1%
3248
 
0.1%
4183
 
< 0.1%
52
 
< 0.1%
696
 
< 0.1%
770
 
< 0.1%
82
 
< 0.1%
942
 
< 0.1%
ValueCountFrequency (%)
23851
< 0.1%
20981
< 0.1%
19011
< 0.1%
17431
< 0.1%
17142
< 0.1%
17061
< 0.1%
17001
< 0.1%
15931
< 0.1%
14721
< 0.1%
14141
< 0.1%

longitud
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.66839045
Minimum27.8189
Maximum43.5667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-10-09T13:06:18.582601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27.8189
5-th percentile28.4775
Q138.282
median40.8206
Q342.0831
95-th percentile43.3669
Maximum43.5667
Range15.7478
Interquartile range (IQR)3.8011

Descriptive statistics

Standard deviation3.765967051
Coefficient of variation (CV)0.09493622019
Kurtosis3.142702015
Mean39.66839045
Median Absolute Deviation (MAD)1.6313
Skewness-1.846323165
Sum16211003.45
Variance14.18250783
MonotonicityNot monotonic
2021-10-09T13:06:18.693653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.82066040
 
1.5%
38.95195807
 
1.4%
40.41175283
 
1.3%
41.11445263
 
1.3%
28.30895246
 
1.3%
41.41815244
 
1.3%
38.98925182
 
1.3%
40.94784992
 
1.2%
43.30754929
 
1.2%
37.97694740
 
1.2%
Other values (191)355937
87.1%
ValueCountFrequency (%)
27.81892486
0.6%
27.92252561
0.6%
28.04752138
0.5%
28.30895246
1.3%
28.44442850
0.7%
28.46314729
1.2%
28.47754132
1.0%
28.63312807
0.7%
28.95172718
0.7%
35.27783097
0.8%
ValueCountFrequency (%)
43.56672750
0.7%
43.56062231
0.5%
43.53813259
0.8%
43.49172663
0.7%
43.46443813
0.9%
43.42923074
0.8%
43.36694734
1.2%
43.36063279
0.8%
43.35422542
0.6%
43.30754929
1.2%

latitud
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct206
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.446611876
Minimum-17.8889
Maximum4.2156
Zeros0
Zeros (%)0.0%
Negative286623
Negative (%)70.1%
Memory size3.1 MiB
2021-10-09T13:06:18.809776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-17.8889
5-th percentile-16.2553
Q1-5.6492
median-3.4503
Q30.4914
95-th percentile2.3767
Maximum4.2156
Range22.1045
Interquartile range (IQR)6.1406

Descriptive statistics

Standard deviation4.697730941
Coefficient of variation (CV)-1.362999697
Kurtosis1.513917608
Mean-3.446611876
Median Absolute Deviation (MAD)2.6053
Skewness-1.171040453
Sum-1408502.749
Variance22.06867599
MonotonicityNot monotonic
2021-10-09T13:06:18.921512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.78926539
 
1.6%
0.49146040
 
1.5%
-1.86315807
 
1.4%
-3.67815283
 
1.3%
-1.41065263
 
1.3%
-16.49925246
 
1.3%
2.12395244
 
1.3%
-2.03924929
 
1.2%
0.71064740
 
1.2%
-8.41924734
 
1.2%
Other values (196)354838
86.8%
ValueCountFrequency (%)
-17.88892486
0.6%
-17.7552807
0.7%
-16.56062138
0.5%
-16.49925246
1.3%
-16.32924132
1.0%
-16.25534729
1.2%
-15.38922561
0.6%
-13.86312850
0.7%
-13.60032718
0.7%
-8.64941393
 
0.3%
ValueCountFrequency (%)
4.21562828
0.7%
3.1817663
 
0.2%
3.1658663
 
0.2%
3.0967663
 
0.2%
3.0353663
 
0.2%
3.0325663
 
0.2%
2.8342246
 
0.1%
2.8267573
 
0.1%
2.82533023
0.7%
2.8067522
 
0.1%

altitud
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.6312517
Minimum1
Maximum2535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-10-09T13:06:19.033335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q142
median247
Q3656
95-th percentile1143
Maximum2535
Range2534
Interquartile range (IQR)614

Descriptive statistics

Standard deviation504.4009139
Coefficient of variation (CV)1.202009888
Kurtosis4.626454871
Mean419.6312517
Median Absolute Deviation (MAD)233
Skewness1.96114448
Sum171487766.2
Variance254420.282
MonotonicityNot monotonic
2021-10-09T13:06:19.142294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412103
 
3.0%
18690
 
2.1%
357252
 
1.8%
326994
 
1.7%
446040
 
1.5%
645951
 
1.5%
55911
 
1.4%
7045807
 
1.4%
875683
 
1.4%
75650
 
1.4%
Other values (163)338582
82.9%
ValueCountFrequency (%)
18690
2.1%
21326
 
0.3%
33200
 
0.8%
412103
3.0%
55911
1.4%
62793
 
0.7%
75650
1.4%
8495
 
0.1%
114482
 
1.1%
143381
 
0.8%
ValueCountFrequency (%)
2535660
 
0.2%
2519659
 
0.2%
2451679
 
0.2%
2400653
 
0.2%
23715246
1.3%
2316663
 
0.2%
2266663
 
0.2%
2247647
 
0.2%
2230663
 
0.2%
2228662
 
0.2%

Interactions

2021-10-09T13:06:05.542375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:05.675799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-09T13:06:06.114526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:06.240578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:06.361668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:06.491354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:06.608955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:06.730429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:06.852112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:06.977747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:07.096681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-09T13:06:08.064432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-09T13:06:08.454028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-09T13:06:09.570946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:09.759924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:09.885491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:10.002476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:10.123042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:10.254075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:10.384996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:10.512638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:10.636057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:10.765057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:10.889930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-09T13:06:11.391188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-09T13:06:11.631299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-09T13:06:15.129763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:15.249866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:15.371049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:15.486941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:15.609163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:06:15.722441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-09T13:06:19.245705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-09T13:06:19.372241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-09T13:06:19.497084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's Ļ„

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (Ļ„) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate Ļ„ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. Ļ„ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-09T13:06:19.626462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-09T13:06:15.891450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-09T13:06:16.267614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

id_estacionfechafecha_cnttmaxtminprecipnevadaprof_nievelongitudlatitudaltitud
0SP0000031951920-01-04196.041.04.00.00.040.4117-3.6781667.0
1SP0000031951920-01-11281.05.00.00.00.040.4117-3.6781667.0
2SP0000031951920-01-183117.021.00.00.00.040.4117-3.6781667.0
3SP0000031951920-01-254118.019.00.00.00.040.4117-3.6781667.0
4SP0000031951920-02-015106.031.02.00.00.040.4117-3.6781667.0
5SP0000031951920-02-086113.08.00.00.00.040.4117-3.6781667.0
6SP0000031951920-02-157119.06.00.00.00.040.4117-3.6781667.0
7SP0000031951920-02-228114.062.099.00.00.040.4117-3.6781667.0
8SP0000031951920-02-299125.074.029.00.00.040.4117-3.6781667.0
9SP0000031951920-03-0710137.064.022.00.00.040.4117-3.6781667.0

Last rows

id_estacionfechafecha_cnttmaxtminprecipnevadaprof_nievelongitudlatitudaltitud
408653SPW000140111967-10-2943181.083.011.00.00.040.4833-3.45608.1
408654SPW000140111967-11-0544133.023.016.00.00.040.4833-3.45608.1
408655SPW000140111967-11-1245141.026.020.00.00.040.4833-3.45608.1
408656SPW000140111967-11-1946130.057.068.00.00.040.4833-3.45608.1
408657SPW000140111967-11-2647135.065.018.00.00.040.4833-3.45608.1
408658SPW000140111967-12-0348126.022.01.00.00.040.4833-3.45608.1
408659SPW000140111967-12-104987.0-13.00.00.00.040.4833-3.45608.1
408660SPW000140111967-12-175064.0-60.00.00.00.040.4833-3.45608.1
408661SPW000140111967-12-245164.0-16.02.00.00.040.4833-3.45608.1
408662SPW000140111967-12-315282.0-19.00.00.00.040.4833-3.45608.1